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A Fully Connected Neural Network to Mitigate 200G DP-16-QAM Transmission System Impairments

Clara Catanese 1 Reda Ayassi 1 Erwan Pincemin 1 yves Jaouën 2, 3, 4 
1 Orange Labs [Lannion]
2 IP Paris - Institut Polytechnique de Paris
3 GTO - Télécommunications Optiques
LTCI - Laboratoire Traitement et Communication de l'Information
4 COMELEC - Département Communications & Electronique
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Conference papers
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https://hal.telecom-paris.fr/hal-02968692
Contributor : Yves Jaouen Connect in order to contact the contributor
Submitted on : Friday, October 16, 2020 - 6:59:05 AM
Last modification on : Wednesday, November 3, 2021 - 6:18:42 AM

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INSTITUT-TELECOM | LTCI | COMELEC | GTO | IP_PARIS

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Clara Catanese, Reda Ayassi, Erwan Pincemin, yves Jaouën. A Fully Connected Neural Network to Mitigate 200G DP-16-QAM Transmission System Impairments. Signal Processing in Photonic Communications, 2020, Washington, France. SpTh3I.1, ⟨10.1364/SPPCOM.2020.SpTh3I.1⟩. ⟨hal-02968692⟩

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